Retail Demand Forecasting via Conversational Signals Using Cloud AI Architectures

  • Authors

    • Jennifer Clark Senior Data Scientist, Innovate Analytics, USA. Author
    • Robert Miller Software Engineering Manager, TechNova Inc, USA. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2021PII1L9S

    Published 10-03-2021

  • Retail Demand Forecasting, Conversational Signals, Cloud AI Architectures, Natural Language Processing (NLP), Machine Learning Models, Customer Interaction Data, Sentiment Analysis, Forecasting Accuracy, AI-Driven Retail Insights, Cloud-Based AI Solutions

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    [1]
    J. Clark and R. Miller, “Retail Demand Forecasting via Conversational Signals Using Cloud AI Architectures”, IJAIDT, vol. 4, no. 2, pp. 01–09, Oct. 2021, doi: 10.67228/30713315/IJAIDT-2021PII1L9S.

  • Abstract

    Retail demand forecasting has traditionally relied on historical sales data and statistical models. However, with the rapid evolution of conversational AI technologies, there is an opportunity to incorporate conversational signals, such as customer inquiries, reviews, and interactions with chatbots or voice assistants, to improve forecasting accuracy. This paper explores the use of conversational signals in retail demand forecasting by leveraging cloud AI architectures. We propose a framework that integrates these signals into machine learning models for demand prediction. By employing natural language processing (NLP) and sentiment analysis techniques, we analyze the role of customer conversations in predicting demand patterns. Through case studies and experiments, we demonstrate the potential of conversational signals in enhancing forecasting models, highlighting the benefits, challenges, and future directions for this emerging area.

  • References

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